Overview

Brought to you by YData

Dataset statistics

Number of variables19
Number of observations10000
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory6.2 MiB
Average record size in memory653.2 B

Variable types

Text1
Categorical6
Numeric9
Boolean3

Alerts

avg_data_usage_gb is highly overall correlated with monthly_spend and 1 other fieldsHigh correlation
duration is highly overall correlated with product_id and 1 other fieldsHigh correlation
gaming is highly overall correlated with product_id and 2 other fieldsHigh correlation
monthly_spend is highly overall correlated with avg_data_usage_gb and 1 other fieldsHigh correlation
product_id is highly overall correlated with duration and 5 other fieldsHigh correlation
product_name is highly overall correlated with duration and 5 other fieldsHigh correlation
social_media is highly overall correlated with product_id and 2 other fieldsHigh correlation
spending_tier is highly overall correlated with avg_data_usage_gb and 1 other fieldsHigh correlation
streaming is highly overall correlated with product_id and 2 other fieldsHigh correlation
target_offer is highly overall correlated with gaming and 4 other fieldsHigh correlation
customer_id has unique valuesUnique
travel_score has unique valuesUnique
pct_video_usage has 236 (2.4%) zerosZeros
topup_freq has 476 (4.8%) zerosZeros
complaint_count has 6132 (61.3%) zerosZeros

Reproduction

Analysis started2025-11-13 17:17:37.149284
Analysis finished2025-11-13 17:18:00.162038
Duration23.01 seconds
Software versionydata-profiling vv4.17.0
Download configurationconfig.json

Variables

customer_id
Text

Unique 

Distinct10000
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size537.2 KiB
2025-11-13T17:18:00.635587image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Length

Max length6
Median length6
Mean length6
Min length6

Characters and Unicode

Total characters60000
Distinct characters11
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique10000 ?
Unique (%)100.0%

Sample

1st rowC00001
2nd rowC00002
3rd rowC00003
4th rowC00004
5th rowC00005
ValueCountFrequency (%)
c000011
 
< 0.1%
c000091
 
< 0.1%
c000181
 
< 0.1%
c000031
 
< 0.1%
c000041
 
< 0.1%
c000051
 
< 0.1%
c000061
 
< 0.1%
c000071
 
< 0.1%
c000081
 
< 0.1%
c000101
 
< 0.1%
Other values (9990)9990
99.9%
2025-11-13T17:18:01.417699image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
013999
23.3%
C10000
16.7%
14001
 
6.7%
64000
 
6.7%
74000
 
6.7%
44000
 
6.7%
54000
 
6.7%
84000
 
6.7%
94000
 
6.7%
24000
 
6.7%

Most occurring categories

ValueCountFrequency (%)
(unknown)60000
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
013999
23.3%
C10000
16.7%
14001
 
6.7%
64000
 
6.7%
74000
 
6.7%
44000
 
6.7%
54000
 
6.7%
84000
 
6.7%
94000
 
6.7%
24000
 
6.7%

Most occurring scripts

ValueCountFrequency (%)
(unknown)60000
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
013999
23.3%
C10000
16.7%
14001
 
6.7%
64000
 
6.7%
74000
 
6.7%
44000
 
6.7%
54000
 
6.7%
84000
 
6.7%
94000
 
6.7%
24000
 
6.7%

Most occurring blocks

ValueCountFrequency (%)
(unknown)60000
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
013999
23.3%
C10000
16.7%
14001
 
6.7%
64000
 
6.7%
74000
 
6.7%
44000
 
6.7%
54000
 
6.7%
84000
 
6.7%
94000
 
6.7%
24000
 
6.7%

plan_type
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size550.8 KiB
Prepaid
6108 
Postpaid
3892 

Length

Max length8
Median length7
Mean length7.3892
Min length7

Characters and Unicode

Total characters73892
Distinct characters10
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowPrepaid
2nd rowPostpaid
3rd rowPostpaid
4th rowPrepaid
5th rowPrepaid

Common Values

ValueCountFrequency (%)
Prepaid6108
61.1%
Postpaid3892
38.9%

Length

2025-11-13T17:18:01.648313image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-11-13T17:18:01.819461image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
prepaid6108
61.1%
postpaid3892
38.9%

Most occurring characters

ValueCountFrequency (%)
P10000
13.5%
p10000
13.5%
a10000
13.5%
i10000
13.5%
d10000
13.5%
r6108
8.3%
e6108
8.3%
o3892
 
5.3%
s3892
 
5.3%
t3892
 
5.3%

Most occurring categories

ValueCountFrequency (%)
(unknown)73892
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
P10000
13.5%
p10000
13.5%
a10000
13.5%
i10000
13.5%
d10000
13.5%
r6108
8.3%
e6108
8.3%
o3892
 
5.3%
s3892
 
5.3%
t3892
 
5.3%

Most occurring scripts

ValueCountFrequency (%)
(unknown)73892
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
P10000
13.5%
p10000
13.5%
a10000
13.5%
i10000
13.5%
d10000
13.5%
r6108
8.3%
e6108
8.3%
o3892
 
5.3%
s3892
 
5.3%
t3892
 
5.3%

Most occurring blocks

ValueCountFrequency (%)
(unknown)73892
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
P10000
13.5%
p10000
13.5%
a10000
13.5%
i10000
13.5%
d10000
13.5%
r6108
8.3%
e6108
8.3%
o3892
 
5.3%
s3892
 
5.3%
t3892
 
5.3%

device_brand
Categorical

Distinct7
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size531.9 KiB
Realme
1509 
Xiaomi
1458 
Samsung
1439 
Huawei
1438 
Vivo
1395 
Other values (2)
2761 

Length

Max length7
Median length6
Mean length5.4513
Min length4

Characters and Unicode

Total characters54513
Distinct characters20
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowRealme
2nd rowVivo
3rd rowXiaomi
4th rowApple
5th rowHuawei

Common Values

ValueCountFrequency (%)
Realme1509
15.1%
Xiaomi1458
14.6%
Samsung1439
14.4%
Huawei1438
14.4%
Vivo1395
14.0%
Apple1386
13.9%
Oppo1375
13.8%

Length

2025-11-13T17:18:02.014634image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-11-13T17:18:02.292441image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
realme1509
15.1%
xiaomi1458
14.6%
samsung1439
14.4%
huawei1438
14.4%
vivo1395
14.0%
apple1386
13.9%
oppo1375
13.8%

Most occurring characters

ValueCountFrequency (%)
a5844
10.7%
e5842
10.7%
i5749
10.5%
p5522
 
10.1%
m4406
 
8.1%
o4228
 
7.8%
l2895
 
5.3%
u2877
 
5.3%
R1509
 
2.8%
X1458
 
2.7%
Other values (10)14183
26.0%

Most occurring categories

ValueCountFrequency (%)
(unknown)54513
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
a5844
10.7%
e5842
10.7%
i5749
10.5%
p5522
 
10.1%
m4406
 
8.1%
o4228
 
7.8%
l2895
 
5.3%
u2877
 
5.3%
R1509
 
2.8%
X1458
 
2.7%
Other values (10)14183
26.0%

Most occurring scripts

ValueCountFrequency (%)
(unknown)54513
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
a5844
10.7%
e5842
10.7%
i5749
10.5%
p5522
 
10.1%
m4406
 
8.1%
o4228
 
7.8%
l2895
 
5.3%
u2877
 
5.3%
R1509
 
2.8%
X1458
 
2.7%
Other values (10)14183
26.0%

Most occurring blocks

ValueCountFrequency (%)
(unknown)54513
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
a5844
10.7%
e5842
10.7%
i5749
10.5%
p5522
 
10.1%
m4406
 
8.1%
o4228
 
7.8%
l2895
 
5.3%
u2877
 
5.3%
R1509
 
2.8%
X1458
 
2.7%
Other values (10)14183
26.0%

avg_data_usage_gb
Real number (ℝ)

High correlation 

Distinct1712
Distinct (%)17.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean5.958883
Minimum0.03
Maximum39.02
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size78.3 KiB
2025-11-13T17:18:02.688835image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0.03
5-th percentile1.13
Q12.87
median4.99
Q38.04
95-th percentile14.19
Maximum39.02
Range38.99
Interquartile range (IQR)5.17

Descriptive statistics

Standard deviation4.1929093
Coefficient of variation (CV)0.70364014
Kurtosis2.7748617
Mean5.958883
Median Absolute Deviation (MAD)2.45
Skewness1.3873462
Sum59588.83
Variance17.580488
MonotonicityNot monotonic
2025-11-13T17:18:02.947553image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
2.3926
 
0.3%
3.0223
 
0.2%
2.1621
 
0.2%
2.6521
 
0.2%
1.7820
 
0.2%
5.6220
 
0.2%
4.1820
 
0.2%
4.6920
 
0.2%
3.2719
 
0.2%
2.0519
 
0.2%
Other values (1702)9791
97.9%
ValueCountFrequency (%)
0.031
 
< 0.1%
0.042
< 0.1%
0.061
 
< 0.1%
0.081
 
< 0.1%
0.121
 
< 0.1%
0.132
< 0.1%
0.143
< 0.1%
0.153
< 0.1%
0.161
 
< 0.1%
0.173
< 0.1%
ValueCountFrequency (%)
39.021
< 0.1%
33.151
< 0.1%
30.661
< 0.1%
30.441
< 0.1%
29.971
< 0.1%
27.841
< 0.1%
26.991
< 0.1%
26.921
< 0.1%
26.821
< 0.1%
26.081
< 0.1%

pct_video_usage
Real number (ℝ)

Zeros 

Distinct9749
Distinct (%)97.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.40263889
Minimum0
Maximum1
Zeros236
Zeros (%)2.4%
Negative0
Negative (%)0.0%
Memory size78.3 KiB
2025-11-13T17:18:03.162142image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0.070404882
Q10.26529046
median0.4001155
Q30.53516088
95-th percentile0.72682106
Maximum1
Range1
Interquartile range (IQR)0.26987042

Descriptive statistics

Standard deviation0.19574145
Coefficient of variation (CV)0.48614642
Kurtosis-0.24568729
Mean0.40263889
Median Absolute Deviation (MAD)0.13493808
Skewness0.11790369
Sum4026.3889
Variance0.038314717
MonotonicityNot monotonic
2025-11-13T17:18:03.376616image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0236
 
2.4%
117
 
0.2%
0.8041460261
 
< 0.1%
0.3418268021
 
< 0.1%
0.7093323631
 
< 0.1%
0.6100092131
 
< 0.1%
0.6290368561
 
< 0.1%
0.0898180631
 
< 0.1%
0.4386418991
 
< 0.1%
0.4755660511
 
< 0.1%
Other values (9739)9739
97.4%
ValueCountFrequency (%)
0236
2.4%
0.0001684781
 
< 0.1%
0.0010374551
 
< 0.1%
0.0013706261
 
< 0.1%
0.0019508451
 
< 0.1%
0.002912081
 
< 0.1%
0.0034963721
 
< 0.1%
0.0046384171
 
< 0.1%
0.0046739631
 
< 0.1%
0.0046912421
 
< 0.1%
ValueCountFrequency (%)
117
0.2%
0.9976667021
 
< 0.1%
0.9897961851
 
< 0.1%
0.9882096161
 
< 0.1%
0.9819821151
 
< 0.1%
0.9780865391
 
< 0.1%
0.975876731
 
< 0.1%
0.9744659451
 
< 0.1%
0.9739725071
 
< 0.1%
0.9723432731
 
< 0.1%

avg_call_duration
Real number (ℝ)

Distinct2229
Distinct (%)22.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean9.971357
Minimum-8.37
Maximum27.77
Zeros0
Zeros (%)0.0%
Negative229
Negative (%)2.3%
Memory size78.3 KiB
2025-11-13T17:18:03.587256image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum-8.37
5-th percentile1.74
Q16.65
median10.02
Q313.32
95-th percentile18.02
Maximum27.77
Range36.14
Interquartile range (IQR)6.67

Descriptive statistics

Standard deviation4.9549927
Coefficient of variation (CV)0.4969226
Kurtosis-0.0069078222
Mean9.971357
Median Absolute Deviation (MAD)3.33
Skewness-0.036472282
Sum99713.57
Variance24.551952
MonotonicityNot monotonic
2025-11-13T17:18:04.098757image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
10.7319
 
0.2%
11.0318
 
0.2%
12.9115
 
0.1%
10.7615
 
0.1%
9.2515
 
0.1%
8.8615
 
0.1%
11.8814
 
0.1%
8.0414
 
0.1%
10.2614
 
0.1%
9.5814
 
0.1%
Other values (2219)9847
98.5%
ValueCountFrequency (%)
-8.371
< 0.1%
-7.131
< 0.1%
-7.121
< 0.1%
-6.941
< 0.1%
-6.811
< 0.1%
-6.731
< 0.1%
-6.681
< 0.1%
-6.531
< 0.1%
-6.451
< 0.1%
-6.041
< 0.1%
ValueCountFrequency (%)
27.771
< 0.1%
27.731
< 0.1%
27.31
< 0.1%
26.841
< 0.1%
26.811
< 0.1%
26.321
< 0.1%
25.991
< 0.1%
25.961
< 0.1%
25.891
< 0.1%
25.831
< 0.1%

sms_freq
Real number (ℝ)

Distinct28
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean15.0108
Minimum4
Maximum31
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size78.3 KiB
2025-11-13T17:18:04.284243image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum4
5-th percentile9
Q112
median15
Q318
95-th percentile22
Maximum31
Range27
Interquartile range (IQR)6

Descriptive statistics

Standard deviation3.8727746
Coefficient of variation (CV)0.25799921
Kurtosis0.090199895
Mean15.0108
Median Absolute Deviation (MAD)3
Skewness0.27597848
Sum150108
Variance14.998383
MonotonicityNot monotonic
2025-11-13T17:18:04.449093image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=28)
ValueCountFrequency (%)
151029
10.3%
141000
10.0%
13990
9.9%
16943
9.4%
17859
8.6%
12829
8.3%
18688
 
6.9%
11685
 
6.9%
19575
 
5.8%
10472
 
4.7%
Other values (18)1930
19.3%
ValueCountFrequency (%)
410
 
0.1%
515
 
0.1%
646
 
0.5%
7110
 
1.1%
8191
 
1.9%
9309
 
3.1%
10472
4.7%
11685
6.9%
12829
8.3%
13990
9.9%
ValueCountFrequency (%)
314
 
< 0.1%
301
 
< 0.1%
298
 
0.1%
284
 
< 0.1%
2718
 
0.2%
2632
 
0.3%
2550
 
0.5%
2482
0.8%
23126
1.3%
22199
2.0%

monthly_spend
Real number (ℝ)

High correlation 

Distinct308
Distinct (%)3.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean109776.5
Minimum-13000
Maximum450000
Zeros0
Zeros (%)0.0%
Negative2
Negative (%)< 0.1%
Memory size78.3 KiB
2025-11-13T17:18:04.643736image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum-13000
5-th percentile49000
Q178000
median102000
Q3135000
95-th percentile196000
Maximum450000
Range463000
Interquartile range (IQR)57000

Descriptive statistics

Standard deviation46237.322
Coefficient of variation (CV)0.42119508
Kurtosis2.038817
Mean109776.5
Median Absolute Deviation (MAD)28000
Skewness1.0602815
Sum1.097765 × 109
Variance2.1378899 × 109
MonotonicityNot monotonic
2025-11-13T17:18:04.873703image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
81000124
 
1.2%
82000121
 
1.2%
85000115
 
1.1%
90000113
 
1.1%
95000112
 
1.1%
89000110
 
1.1%
98000105
 
1.1%
100000104
 
1.0%
78000103
 
1.0%
87000103
 
1.0%
Other values (298)8890
88.9%
ValueCountFrequency (%)
-130001
 
< 0.1%
-60001
 
< 0.1%
20001
 
< 0.1%
50001
 
< 0.1%
70001
 
< 0.1%
80002
< 0.1%
90001
 
< 0.1%
120003
< 0.1%
130002
< 0.1%
140001
 
< 0.1%
ValueCountFrequency (%)
4500001
< 0.1%
3720001
< 0.1%
3630001
< 0.1%
3540001
< 0.1%
3500001
< 0.1%
3490001
< 0.1%
3370001
< 0.1%
3330002
< 0.1%
3300001
< 0.1%
3240002
< 0.1%

topup_freq
Real number (ℝ)

Zeros 

Distinct12
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.9866
Minimum0
Maximum12
Zeros476
Zeros (%)4.8%
Negative0
Negative (%)0.0%
Memory size78.3 KiB
2025-11-13T17:18:05.039115image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1
Q12
median3
Q34
95-th percentile6
Maximum12
Range12
Interquartile range (IQR)2

Descriptive statistics

Standard deviation1.7098283
Coefficient of variation (CV)0.57249993
Kurtosis0.32893541
Mean2.9866
Median Absolute Deviation (MAD)1
Skewness0.57530473
Sum29866
Variance2.9235128
MonotonicityNot monotonic
2025-11-13T17:18:05.179009image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=12)
ValueCountFrequency (%)
32293
22.9%
22236
22.4%
41701
17.0%
11512
15.1%
5970
9.7%
6493
 
4.9%
0476
 
4.8%
7209
 
2.1%
872
 
0.7%
930
 
0.3%
Other values (2)8
 
0.1%
ValueCountFrequency (%)
0476
 
4.8%
11512
15.1%
22236
22.4%
32293
22.9%
41701
17.0%
5970
9.7%
6493
 
4.9%
7209
 
2.1%
872
 
0.7%
930
 
0.3%
ValueCountFrequency (%)
121
 
< 0.1%
107
 
0.1%
930
 
0.3%
872
 
0.7%
7209
 
2.1%
6493
 
4.9%
5970
9.7%
41701
17.0%
32293
22.9%
22236
22.4%

travel_score
Real number (ℝ)

Unique 

Distinct10000
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.2824407
Minimum0.003375549
Maximum0.85481359
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size78.3 KiB
2025-11-13T17:18:05.354866image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0.003375549
5-th percentile0.061392273
Q10.15877515
median0.2612058
Q30.3856562
95-th percentile0.57436417
Maximum0.85481359
Range0.85143804
Interquartile range (IQR)0.22688105

Descriptive statistics

Standard deviation0.15836636
Coefficient of variation (CV)0.56070658
Kurtosis-0.090670875
Mean0.2824407
Median Absolute Deviation (MAD)0.11067369
Skewness0.60349188
Sum2824.407
Variance0.025079903
MonotonicityNot monotonic
2025-11-13T17:18:05.553001image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.2844189971
 
< 0.1%
0.3964357841
 
< 0.1%
0.0919343211
 
< 0.1%
0.0859502981
 
< 0.1%
0.4080435871
 
< 0.1%
0.5491357171
 
< 0.1%
0.042872081
 
< 0.1%
0.0541407441
 
< 0.1%
0.3398289781
 
< 0.1%
0.1521644231
 
< 0.1%
Other values (9990)9990
99.9%
ValueCountFrequency (%)
0.0033755491
< 0.1%
0.0041119881
< 0.1%
0.0050141071
< 0.1%
0.0052954721
< 0.1%
0.0052979411
< 0.1%
0.0065182271
< 0.1%
0.0065906491
< 0.1%
0.0067899531
< 0.1%
0.0069004761
< 0.1%
0.0071614061
< 0.1%
ValueCountFrequency (%)
0.8548135931
< 0.1%
0.8441668741
< 0.1%
0.8431253541
< 0.1%
0.8392159611
< 0.1%
0.8337350331
< 0.1%
0.8332014551
< 0.1%
0.8265612141
< 0.1%
0.8238223461
< 0.1%
0.8233661411
< 0.1%
0.8230096781
< 0.1%

complaint_count
Real number (ℝ)

Zeros 

Distinct6
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.4926
Minimum0
Maximum5
Zeros6132
Zeros (%)61.3%
Negative0
Negative (%)0.0%
Memory size78.3 KiB
2025-11-13T17:18:05.710821image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q31
95-th percentile2
Maximum5
Range5
Interquartile range (IQR)1

Descriptive statistics

Standard deviation0.70412699
Coefficient of variation (CV)1.4294092
Kurtosis1.8459323
Mean0.4926
Median Absolute Deviation (MAD)0
Skewness1.4076191
Sum4926
Variance0.49579482
MonotonicityNot monotonic
2025-11-13T17:18:05.852950image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=6)
ValueCountFrequency (%)
06132
61.3%
12964
29.6%
2766
 
7.7%
3123
 
1.2%
414
 
0.1%
51
 
< 0.1%
ValueCountFrequency (%)
06132
61.3%
12964
29.6%
2766
 
7.7%
3123
 
1.2%
414
 
0.1%
51
 
< 0.1%
ValueCountFrequency (%)
51
 
< 0.1%
414
 
0.1%
3123
 
1.2%
2766
 
7.7%
12964
29.6%
06132
61.3%

product_id
Real number (ℝ)

High correlation 

Distinct25
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean12.886
Minimum1
Maximum25
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size78.3 KiB
2025-11-13T17:18:06.006904image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile2
Q17
median13
Q319
95-th percentile24
Maximum25
Range24
Interquartile range (IQR)12

Descriptive statistics

Standard deviation7.1678269
Coefficient of variation (CV)0.55624917
Kurtosis-1.1893821
Mean12.886
Median Absolute Deviation (MAD)6
Skewness0.024858226
Sum128860
Variance51.377742
MonotonicityNot monotonic
2025-11-13T17:18:06.178897image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=25)
ValueCountFrequency (%)
7434
 
4.3%
12431
 
4.3%
3426
 
4.3%
14419
 
4.2%
4418
 
4.2%
6412
 
4.1%
18411
 
4.1%
19410
 
4.1%
22407
 
4.1%
10407
 
4.1%
Other values (15)5825
58.2%
ValueCountFrequency (%)
1406
4.1%
2368
3.7%
3426
4.3%
4418
4.2%
5377
3.8%
6412
4.1%
7434
4.3%
8401
4.0%
9402
4.0%
10407
4.1%
ValueCountFrequency (%)
25395
4.0%
24369
3.7%
23384
3.8%
22407
4.1%
21365
3.6%
20403
4.0%
19410
4.1%
18411
4.1%
17359
3.6%
16386
3.9%

product_name
Categorical

High correlation 

Distinct25
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size865.2 KiB
100gb 220k (internet only 7)
 
434
90gb 240k (60gb internet + 10gb stream + 10gb game + 10gb socmed, pahe 4)
 
431
25gb 80k (internet only 3)
 
426
5gb 10k (internet only harian 1)
 
419
35gb 100k (internet only 4)
 
418
Other values (20)
7872 

Length

Max length75
Median length59
Mean length39.5877
Min length26

Characters and Unicode

Total characters395877
Distinct characters34
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row10gb 50k (internet only 1)
2nd row20gb 45k (internet + stream mingguan)
3rd row10gb 15k (internet only harian 2)
4th row15gb 65k (internet only 2)
5th row3gb 8k (internet only hemat 1)

Common Values

ValueCountFrequency (%)
100gb 220k (internet only 7)434
 
4.3%
90gb 240k (60gb internet + 10gb stream + 10gb game + 10gb socmed, pahe 4)431
 
4.3%
25gb 80k (internet only 3)426
 
4.3%
5gb 10k (internet only harian 1)419
 
4.2%
35gb 100k (internet only 4)418
 
4.2%
80gb 200k (internet only 6)412
 
4.1%
20gb 45k (internet + stream mingguan)411
 
4.1%
12gb 30k (internet only 14 hari 1)410
 
4.1%
6gb 20k (internet + game mingguan)407
 
4.1%
30gb 100k (25gb internet + 5gb stream + 5gb game, pahe 2)407
 
4.1%
Other values (15)5825
58.2%

Length

2025-11-13T17:18:06.362527image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
internet10000
 
13.1%
8033
 
10.5%
only5607
 
7.4%
10gb4475
 
5.9%
stream4022
 
5.3%
13177
 
4.2%
5gb2406
 
3.2%
mingguan2323
 
3.0%
game2050
 
2.7%
pahe2045
 
2.7%
Other values (51)32134
42.1%

Most occurring characters

ValueCountFrequency (%)
66672
16.8%
n31446
 
7.9%
e30895
 
7.8%
t24807
 
6.3%
g24464
 
6.2%
018423
 
4.7%
b17768
 
4.5%
r16028
 
4.0%
a14424
 
3.6%
i14329
 
3.6%
Other values (24)136621
34.5%

Most occurring categories

ValueCountFrequency (%)
(unknown)395877
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
66672
16.8%
n31446
 
7.9%
e30895
 
7.8%
t24807
 
6.3%
g24464
 
6.2%
018423
 
4.7%
b17768
 
4.5%
r16028
 
4.0%
a14424
 
3.6%
i14329
 
3.6%
Other values (24)136621
34.5%

Most occurring scripts

ValueCountFrequency (%)
(unknown)395877
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
66672
16.8%
n31446
 
7.9%
e30895
 
7.8%
t24807
 
6.3%
g24464
 
6.2%
018423
 
4.7%
b17768
 
4.5%
r16028
 
4.0%
a14424
 
3.6%
i14329
 
3.6%
Other values (24)136621
34.5%

Most occurring blocks

ValueCountFrequency (%)
(unknown)395877
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
66672
16.8%
n31446
 
7.9%
e30895
 
7.8%
t24807
 
6.3%
g24464
 
6.2%
018423
 
4.7%
b17768
 
4.5%
r16028
 
4.0%
a14424
 
3.6%
i14329
 
3.6%
Other values (24)136621
34.5%

spending_tier
Categorical

High correlation 

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size509.6 KiB
mid
5597 
low
2677 
high
1726 

Length

Max length4
Median length3
Mean length3.1726
Min length3

Characters and Unicode

Total characters31726
Distinct characters8
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowlow
2nd rowlow
3rd rowmid
4th rowlow
5th rowlow

Common Values

ValueCountFrequency (%)
mid5597
56.0%
low2677
26.8%
high1726
 
17.3%

Length

2025-11-13T17:18:06.525043image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-11-13T17:18:06.628329image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
mid5597
56.0%
low2677
26.8%
high1726
 
17.3%

Most occurring characters

ValueCountFrequency (%)
i7323
23.1%
m5597
17.6%
d5597
17.6%
h3452
10.9%
l2677
 
8.4%
o2677
 
8.4%
w2677
 
8.4%
g1726
 
5.4%

Most occurring categories

ValueCountFrequency (%)
(unknown)31726
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
i7323
23.1%
m5597
17.6%
d5597
17.6%
h3452
10.9%
l2677
 
8.4%
o2677
 
8.4%
w2677
 
8.4%
g1726
 
5.4%

Most occurring scripts

ValueCountFrequency (%)
(unknown)31726
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
i7323
23.1%
m5597
17.6%
d5597
17.6%
h3452
10.9%
l2677
 
8.4%
o2677
 
8.4%
w2677
 
8.4%
g1726
 
5.4%

Most occurring blocks

ValueCountFrequency (%)
(unknown)31726
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
i7323
23.1%
m5597
17.6%
d5597
17.6%
h3452
10.9%
l2677
 
8.4%
o2677
 
8.4%
w2677
 
8.4%
g1726
 
5.4%

duration
Categorical

High correlation 

Distinct4
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size543.2 KiB
30 hari
5287 
7 hari
2323 
1 hari
1577 
14 hari
813 

Length

Max length7
Median length7
Mean length6.61
Min length6

Characters and Unicode

Total characters66100
Distinct characters10
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row30 hari
2nd row7 hari
3rd row1 hari
4th row30 hari
5th row1 hari

Common Values

ValueCountFrequency (%)
30 hari5287
52.9%
7 hari2323
23.2%
1 hari1577
 
15.8%
14 hari813
 
8.1%

Length

2025-11-13T17:18:06.783631image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-11-13T17:18:06.907816image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
hari10000
50.0%
305287
26.4%
72323
 
11.6%
11577
 
7.9%
14813
 
4.1%

Most occurring characters

ValueCountFrequency (%)
10000
15.1%
h10000
15.1%
a10000
15.1%
r10000
15.1%
i10000
15.1%
35287
8.0%
05287
8.0%
12390
 
3.6%
72323
 
3.5%
4813
 
1.2%

Most occurring categories

ValueCountFrequency (%)
(unknown)66100
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
10000
15.1%
h10000
15.1%
a10000
15.1%
r10000
15.1%
i10000
15.1%
35287
8.0%
05287
8.0%
12390
 
3.6%
72323
 
3.5%
4813
 
1.2%

Most occurring scripts

ValueCountFrequency (%)
(unknown)66100
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
10000
15.1%
h10000
15.1%
a10000
15.1%
r10000
15.1%
i10000
15.1%
35287
8.0%
05287
8.0%
12390
 
3.6%
72323
 
3.5%
4813
 
1.2%

Most occurring blocks

ValueCountFrequency (%)
(unknown)66100
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
10000
15.1%
h10000
15.1%
a10000
15.1%
r10000
15.1%
i10000
15.1%
35287
8.0%
05287
8.0%
12390
 
3.6%
72323
 
3.5%
4813
 
1.2%

streaming
Boolean

High correlation 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size9.9 KiB
False
6379 
True
3621 
ValueCountFrequency (%)
False6379
63.8%
True3621
36.2%
2025-11-13T17:18:07.041754image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

gaming
Boolean

High correlation 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size9.9 KiB
False
7950 
True
2050 
ValueCountFrequency (%)
False7950
79.5%
True2050
 
20.5%
2025-11-13T17:18:07.119101image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

social_media
Boolean

High correlation 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size9.9 KiB
False
8007 
True
1993 
ValueCountFrequency (%)
False8007
80.1%
True1993
 
19.9%
2025-11-13T17:18:07.202960image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

target_offer
Categorical

High correlation 

Distinct5
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size637.4 KiB
General Offer
5607 
Allrounder offer
2050 
Streaming partner offer
1181 
Stream and social media offer
797 
Social media offer
 
365

Length

Max length29
Median length13
Mean length16.2537
Min length13

Characters and Unicode

Total characters162537
Distinct characters21
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowGeneral Offer
2nd rowStreaming partner offer
3rd rowGeneral Offer
4th rowGeneral Offer
5th rowGeneral Offer

Common Values

ValueCountFrequency (%)
General Offer5607
56.1%
Allrounder offer2050
 
20.5%
Streaming partner offer1181
 
11.8%
Stream and social media offer797
 
8.0%
Social media offer365
 
3.6%

Length

2025-11-13T17:18:07.344581image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-11-13T17:18:07.473707image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
offer10000
41.8%
general5607
23.4%
allrounder2050
 
8.6%
streaming1181
 
4.9%
partner1181
 
4.9%
social1162
 
4.9%
media1162
 
4.9%
stream797
 
3.3%
and797
 
3.3%

Most occurring characters

ValueCountFrequency (%)
e27585
17.0%
r24047
14.8%
f20000
12.3%
13937
8.6%
a11887
7.3%
l10869
 
6.7%
n10816
 
6.7%
o7605
 
4.7%
G5607
 
3.4%
O5607
 
3.4%
Other values (11)24577
15.1%

Most occurring categories

ValueCountFrequency (%)
(unknown)162537
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
e27585
17.0%
r24047
14.8%
f20000
12.3%
13937
8.6%
a11887
7.3%
l10869
 
6.7%
n10816
 
6.7%
o7605
 
4.7%
G5607
 
3.4%
O5607
 
3.4%
Other values (11)24577
15.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown)162537
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
e27585
17.0%
r24047
14.8%
f20000
12.3%
13937
8.6%
a11887
7.3%
l10869
 
6.7%
n10816
 
6.7%
o7605
 
4.7%
G5607
 
3.4%
O5607
 
3.4%
Other values (11)24577
15.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown)162537
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
e27585
17.0%
r24047
14.8%
f20000
12.3%
13937
8.6%
a11887
7.3%
l10869
 
6.7%
n10816
 
6.7%
o7605
 
4.7%
G5607
 
3.4%
O5607
 
3.4%
Other values (11)24577
15.1%

Interactions

2025-11-13T17:17:57.810057image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-13T17:17:39.363012image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-13T17:17:41.231072image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-13T17:17:43.700799image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-13T17:17:47.506142image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-13T17:17:51.270213image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-13T17:17:52.862664image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-13T17:17:54.483228image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-13T17:17:56.237672image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-13T17:17:57.996647image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-13T17:17:39.535252image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-13T17:17:41.663405image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-13T17:17:43.940913image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-13T17:17:48.088346image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-13T17:17:51.448726image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-13T17:17:53.045585image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-13T17:17:54.669322image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-13T17:17:56.425771image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-13T17:17:58.181519image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-13T17:17:39.704364image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-13T17:17:41.905527image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-13T17:17:44.186926image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-13T17:17:48.698582image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-13T17:17:51.616710image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-13T17:17:53.249210image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-13T17:17:54.834052image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-13T17:17:56.590901image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-13T17:17:58.363873image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-13T17:17:39.874996image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-13T17:17:42.241396image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-13T17:17:44.502756image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-13T17:17:49.227022image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-13T17:17:51.809783image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-13T17:17:53.431430image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-13T17:17:55.262160image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-13T17:17:56.775353image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-13T17:17:58.538251image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-13T17:17:40.041870image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-13T17:17:42.618438image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-13T17:17:44.794420image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-13T17:17:49.742902image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-13T17:17:51.977764image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-13T17:17:53.605184image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-13T17:17:55.417476image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-13T17:17:56.946521image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-13T17:17:58.698327image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-13T17:17:40.226198image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-13T17:17:42.892188image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-13T17:17:45.114945image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-13T17:17:50.066594image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-13T17:17:52.174429image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-13T17:17:53.781241image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-13T17:17:55.578068image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-13T17:17:57.122962image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-13T17:17:58.880812image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-13T17:17:40.392980image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-13T17:17:43.097466image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-13T17:17:45.429530image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-13T17:17:50.327804image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-13T17:17:52.349244image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-13T17:17:53.957673image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-13T17:17:55.748423image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-13T17:17:57.310512image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-13T17:17:59.054385image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-13T17:17:40.554790image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-13T17:17:43.289770image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-13T17:17:46.204122image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-13T17:17:50.830733image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-13T17:17:52.514760image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-13T17:17:54.138527image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-13T17:17:55.906538image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-13T17:17:57.482279image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-13T17:17:59.224652image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-13T17:17:40.838115image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-13T17:17:43.503779image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-13T17:17:46.791337image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-13T17:17:51.111501image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-13T17:17:52.684031image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-13T17:17:54.319599image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-13T17:17:56.070096image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-13T17:17:57.644938image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Correlations

2025-11-13T17:18:07.636102image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
avg_call_durationavg_data_usage_gbcomplaint_countdevice_branddurationgamingmonthly_spendpct_video_usageplan_typeproduct_idproduct_namesms_freqsocial_mediaspending_tierstreamingtarget_offertopup_freqtravel_score
avg_call_duration1.000-0.013-0.0100.0000.0190.000-0.012-0.0070.0000.0140.0110.0080.0300.0210.0000.000-0.002-0.016
avg_data_usage_gb-0.0131.0000.0080.0000.0140.0210.8540.0240.000-0.0270.016-0.0080.0000.6610.0210.0110.0180.010
complaint_count-0.0100.0081.0000.0100.0000.0080.007-0.0030.015-0.0040.000-0.0020.0000.0000.0000.0000.001-0.017
device_brand0.0000.0000.0101.0000.0120.0000.0000.0000.0000.0000.0040.0000.0000.0000.0000.0000.0020.000
duration0.0190.0140.0000.0121.0000.3140.0030.0000.0000.8700.9990.0130.2950.0170.2840.3410.0000.013
gaming0.0000.0210.0080.0000.3141.0000.0000.0000.0000.8700.9990.0080.2610.0000.4641.0000.0000.000
monthly_spend-0.0120.8540.0070.0000.0030.0001.0000.0190.000-0.0350.000-0.0050.0000.9010.0240.0170.0100.014
pct_video_usage-0.0070.024-0.0030.0000.0000.0000.0191.0000.022-0.0080.0000.0210.0000.0140.0000.000-0.008-0.001
plan_type0.0000.0000.0150.0000.0000.0000.0000.0221.0000.0000.0080.0280.0060.0000.0000.0000.0280.008
product_id0.014-0.027-0.0040.0000.8700.870-0.035-0.0080.0001.0000.9990.0060.6470.0150.8080.686-0.0190.001
product_name0.0110.0160.0000.0040.9990.9990.0000.0000.0080.9991.0000.0100.9990.0000.9990.9990.0000.014
sms_freq0.008-0.008-0.0020.0000.0130.008-0.0050.0210.0280.0060.0101.0000.0000.0040.0000.0000.0120.002
social_media0.0300.0000.0000.0000.2950.2610.0000.0000.0060.6470.9990.0001.0000.0000.4720.8310.0000.035
spending_tier0.0210.6610.0000.0000.0170.0000.9010.0140.0000.0150.0000.0040.0001.0000.0000.0000.0000.014
streaming0.0000.0210.0000.0000.2840.4640.0240.0000.0000.8080.9990.0000.4720.0001.0000.9270.0000.009
target_offer0.0000.0110.0000.0000.3411.0000.0170.0000.0000.6860.9990.0000.8310.0000.9271.0000.0000.000
topup_freq-0.0020.0180.0010.0020.0000.0000.010-0.0080.028-0.0190.0000.0120.0000.0000.0000.0001.0000.001
travel_score-0.0160.010-0.0170.0000.0130.0000.014-0.0010.0080.0010.0140.0020.0350.0140.0090.0000.0011.000

Missing values

2025-11-13T17:17:59.528820image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
A simple visualization of nullity by column.
2025-11-13T17:17:59.905848image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

customer_idplan_typedevice_brandavg_data_usage_gbpct_video_usageavg_call_durationsms_freqmonthly_spendtopup_freqtravel_scorecomplaint_countproduct_idproduct_namespending_tierdurationstreaminggamingsocial_mediatarget_offer
0C00001PrepaidRealme1.500.8041467.98137000040.2844190110gb 50k (internet only 1)low30 harinononoGeneral Offer
1C00002PostpaidVivo1.090.1076869.5696300030.11508601820gb 45k (internet + stream mingguan)low7 hariyesnonoStreaming partner offer
2C00003PostpaidXiaomi3.240.3138944.61138900070.40299801510gb 15k (internet only harian 2)mid1 harinononoGeneral Offer
3C00004PrepaidApple5.320.4201586.9686700040.3021690215gb 65k (internet only 2)low30 harinononoGeneral Offer
4C00005PrepaidHuawei1.910.25163811.01217200050.4879110233gb 8k (internet only hemat 1)low1 harinononoGeneral Offer
5C00006PrepaidOppo3.300.4788736.53175400030.3721350680gb 200k (internet only 6)low30 harinononoGeneral Offer
6C00007PrepaidOppo9.870.37588211.171114000050.30822017100gb 220k (internet only 7)mid30 harinononoGeneral Offer
7C00008PostpaidOppo13.910.4659777.721418000040.2882572550gb 150k (internet only 5)high30 harinononoGeneral Offer
8C00009PostpaidRealme1.250.22217810.73207200020.2706010935gb 120k (25gb internet + 5gb stream + 5gb socmed, pahe 1)low30 hariyesnoyesStream and social media offer
9C00010PostpaidHuawei3.660.19677413.36118300010.3427300550gb 150k (internet only 5)mid30 harinononoGeneral Offer
customer_idplan_typedevice_brandavg_data_usage_gbpct_video_usageavg_call_durationsms_freqmonthly_spendtopup_freqtravel_scorecomplaint_countproduct_idproduct_namespending_tierdurationstreaminggamingsocial_mediatarget_offer
9990C09991PrepaidRealme5.320.35652014.251410800040.1383540226gb 20k (internet + game mingguan)mid7 harinoyesnoAllrounder offer
9991C09992PrepaidVivo12.170.51375415.621616900020.4435590435gb 100k (internet only 4)high30 harinononoGeneral Offer
9992C09993PrepaidSamsung4.120.71353313.021312200040.2994390168gb 25k (internet only mingguan 1)mid7 harinononoGeneral Offer
9993C09994PostpaidOppo2.360.32002013.291810000010.3599350550gb 150k (internet only 5)mid30 harinononoGeneral Offer
9994C09995PrepaidRealme3.160.5730606.661111800000.2659290325gb 80k (internet only 3)mid30 harinononoGeneral Offer
9995C09996PostpaidHuawei12.080.3961576.771114500010.23540701510gb 15k (internet only harian 2)mid1 harinononoGeneral Offer
9996C09997PostpaidVivo4.490.5049123.901710600020.24268011510gb 15k (internet only harian 2)mid1 harinononoGeneral Offer
9997C09998PostpaidXiaomi12.040.80187720.122116300040.15904807100gb 220k (internet only 7)high30 harinononoGeneral Offer
9998C09999PrepaidXiaomi11.070.5861048.041414000050.0314561680gb 200k (internet only 6)mid30 harinononoGeneral Offer
9999C10000PrepaidXiaomi3.290.65136913.47169900040.2753930110gb 50k (internet only 1)mid30 harinononoGeneral Offer